The development of a nonlinear curve fitter using RBF neural networks with hybrid neurons

Abstract

This paper investigates a new method using radial basis function (RBF) neural networks with an additional linear neuron for solving nonlinear curve fitting problem. The complicated unknown function to be fitted is approximated by a set of Gaussian basis function with a linear term correction. The proposed new technique is first used to evaluate two benchmark examples and subsequently applied to fit several heavy ion stopping power datasets (MeV energetic projectiles in aluminium). Due to the linear correction effect, the proposed approach significantly improves accuracy of fitting without adding much computational complexity. The developed method can be served as a standalone curve fitter or implemented as a proprietary software module to be embedded in an intelligent data analysis package for applications in regression analysis.